The　fast　development　of　the　Internet　of　things　(IoT)　promises　to　deliver　convenience　to　human　life.　However,　a　huge　amount　of　the　data　is　constantly　generated,　transmitted,　processed,　and　stored,　posing　significant　security　challenges.　The　currently　available　security　protocols　and　encryption　techniques　are　mostly　based　on　software　algorithms　and　pseudorandom　number　generators　that　are　vulnerable　to　attacks.　A　true　random　number　generator　(TRNG)　based　on　devices　using　stochastically　physical　phenomena　has　been　proposed　for　auditory　data　encryption　and　trusted　communication.　In　the　current　study,　a　Bi2O2Se-based　memristive　TRNG　is　demonstrated　for　security　applications.　Compared　with　traditional　metal-insulator-metal　based　memristors,　or　other　two-dimensional　material-based　memristors,　the　Bi2O2Se　layer　as　electrode　with　non-van　der　Waals　interface,　high　carrier　mobility,　air　stability,　extreme　low　thermal　conductivity,　as　well　as　vertical　surface　resistive　switching　shows　intrinsic　stochasticity　and　complexity　in　a　memristive　true　analogue/digital　random　number　generation.　Moreover,　those　analogue/digital　random　number　generation　processes　are　proved　to　be　resilient　for　machine　learning　prediction.